Genome-wide mRNA profiling provides a snapshot of the global state of mammalian cells under different experimental conditions such as diseased vs. normal or drug vs. mock treatment cellular states. However, since measurements are in the form of quantitative changes in mRNA levels, such experimental data does not provide direct understanding of the regulatory upstream molecular mechanisms responsible for the observed changes. Identifying potential cell signaling regulatory mechanisms responsible for changes in gene expression under different experimental conditions or in different tissues has been the focus of many computational systems biology efforts. Most popular approaches include gene ontology or pathway enrichment analyses, as well as reverse engineering of networks from mRNA expression data. However, these methods often assume that differentially expressed genes give rise to pathways and functional modules which is not always true in higher eukaryotes. Here we propose an alternative rational approach, called Expression2Kinases, to identify and rank transcription factors, chromatin modifiers, protein complexes, and protein kinases that are likely responsible for observed changes in gene expression. By combining data from ChIP-seq and ChIP-chip experiments, protein-protein interactions reported in publicly available databases, and kinase-protein phosphorylation reactions collected from the literature, we can identify and rank upstream regulators based on genome-wide changes in gene expression. The idea is to infer the transcription-factors and chromatin regulators responsible for changes in gene-expression; then use protein-protein interactions to connect the identified factors to build transcriptional complexes involving the factors; then use kinase-protein phosphorylation reactions to identify and rank candidate protein kinases that most likely regulate the formation of the identified transcriptional complexes. We plan to validate this method with phosphoproteomics data, data from drug perturbations followed by genome-wide gene expression, RNAi screens, as well as through literature-based text-mining approaches. The project will produce several high quality datasets, web-based software, new algorithms, and robust lists of transcription-factors, histone modifiers, and kinase rankings likely responsible fo mammalian cell regulation. The approach will be experimentally tested in several collaborative projects mainly exploring regulation of differentiating stem and iPS cells. The databases, software tools and algorithms developed for this project will advance drug target discovery and help in unraveling drug mechanisms of action.